{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import cv2\n",
    "from tqdm import tqdm_notebook\n",
    "import pickle\n",
    "import os\n",
    "import logging\n",
    "import time\n",
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from dataset.mask_functions import rle2mask, mask2rle\n",
    "from dataset.dataset import prepare_trainset\n",
    "from utils.utils import save_checkpoint, load_checkpoint, set_logger\n",
    "from utils.gpu_utils import set_n_get_device\n",
    "\n",
    "from model.model_unet import UNetResNet34, predict_proba\n",
    "#from model.model_unet_classify_zero import UNetResNet34 as ZeroMaskClassifier\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====MODEL ACHITECTURE: UNetResNet34====\n"
     ]
    }
   ],
   "source": [
    "######### Config the training process #########\n",
    "#device = set_n_get_device(\"0, 1, 2, 3\", data_device_id=\"cuda:0\")#0, 1, 2, 3, IMPORTANT: data_device_id is set to free gpu for storing the model, e.g.\"cuda:1\"\n",
    "MODEL = 'UNetResNet34'#'RESNET34', 'RESNET18', 'INCEPTION_V3', 'BNINCEPTION', 'SEResnet50'\n",
    "#AUX_LOGITS = True#False, only for 'INCEPTION_V3'\n",
    "print('====MODEL ACHITECTURE: %s===='%MODEL)\n",
    "\n",
    "device = set_n_get_device(\"2\", data_device_id=\"cuda:0\")#0, 1, 2, 3, IMPORTANT: data_device_id is set to free gpu for storing the model, e.g.\"cuda:1\"\n",
    "multi_gpu = None #[0, 1]#use 2 gpus\n",
    "\n",
    "SEED = 1234 #5678#4567#3456#2345#1234\n",
    "debug = True # if True, load 100 samples\n",
    "IMG_SIZE = None\n",
    "BATCH_SIZE = 8\n",
    "NUM_WORKERS = 24\n",
    "torch.cuda.manual_seed_all(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count images in train/valid folder:  12568 1801\n",
      "Count of trainset (for training):  1120\n",
      "Count of validset (for training):  120\n"
     ]
    }
   ],
   "source": [
    "train_dl, val_dl = prepare_trainset(BATCH_SIZE, NUM_WORKERS, SEED, IMG_SIZE, debug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i, (images, masks) in enumerate(train_dl):\n",
    "    images = images.to(device=device, dtype=torch.float)\n",
    "    masks = masks.to(device=device, dtype=torch.float)\n",
    "    #labels = (torch.sum(masks.reshape(masks.size()[0], -1), dim=1, keepdim=True)==0).to(device=device, dtype=torch.float) #1 for non-zero-mask\n",
    "    if i==0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([8, 1, 256, 1600]), torch.Size([8, 4, 256, 1600]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.size(), masks.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "net = UNetResNet34(debug=True).cuda(device=device)\n",
    "#net = ZeroMaskClassifier(debug=True).cuda(device=device)\n",
    "\n",
    "#torch.cuda.set_device(0)\n",
    "#torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')\n",
    "#net = DistributedDataParallel(net, device_ids=[0], output_device=0)\n",
    "#torch.distributed.init_process_group(backend=\"nccl\")\n",
    "\n",
    "#checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed3456/best.pth.tar'\n",
    "#net, _ = load_checkpoint(checkpoint_path, net)\n",
    "\n",
    "if multi_gpu is not None:\n",
    "    net = nn.DataParallel(net, device_ids=multi_gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input:  torch.Size([8, 3, 256, 1600])\n",
      "e1 torch.Size([8, 64, 128, 800])\n",
      "e2 torch.Size([8, 64, 128, 800])\n",
      "e3 torch.Size([8, 128, 64, 400])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e4 torch.Size([8, 256, 32, 200])\n",
      "e5 torch.Size([8, 512, 16, 100])\n",
      "center torch.Size([8, 256, 8, 50])\n",
      "d5 torch.Size([8, 64, 16, 100])\n",
      "d4 torch.Size([8, 64, 32, 200])\n",
      "d3 torch.Size([8, 64, 64, 400])\n",
      "d2 torch.Size([8, 64, 128, 800])\n",
      "d1 torch.Size([8, 64, 256, 1600])\n",
      "hypercolum torch.Size([8, 320, 256, 1600])\n",
      "logit torch.Size([8, 4, 256, 1600])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:2457: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n"
     ]
    }
   ],
   "source": [
    "logit = net(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6753, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_loss = net.criterion(logit, masks)\n",
    "_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0072, device='cuda:0')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_metric = net.metric(logit, masks)\n",
    "_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## predict the validset, and analyse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#move checkpoint from gamma machine to here\n",
    "cd checkpoint\n",
    "scp -r endi.niu@10.171.36.214:/home/endi.niu/SIIM/checkpoint/UNetResNet34_1024_v1_seed8901/ UNetResNet34_1024_v1_seed8901\n",
    "cd logging\n",
    "scp -r endi.niu@10.171.36.214:/home/endi.niu/SIIM/logging/UNetResNet34_1024_v1_seed8901.log UNetResNet34_1024_v1_seed8901.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import pickle\n",
    "import os\n",
    "import logging\n",
    "import time\n",
    "import gc\n",
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from utils.utils import save_checkpoint, load_checkpoint, set_logger\n",
    "from utils.gpu_utils import set_n_get_device\n",
    "\n",
    "from dataset.dataset import prepare_trainset\n",
    "from model.model_unet import UNetResNet34, predict_proba\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def inverse_sigmoid(x):\n",
    "    return np.log(x / (1-x))\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====MODEL ACHITECTURE: UNetResNet34====\n"
     ]
    }
   ],
   "source": [
    "######### Config the training process #########\n",
    "#device = set_n_get_device(\"0, 1, 2, 3\", data_device_id=\"cuda:0\")#0, 1, 2, 3, IMPORTANT: data_device_id is set to free gpu for storing the model, e.g.\"cuda:1\"\n",
    "MODEL = 'UNetResNet34'#'RESNET34', 'RESNET18', 'INCEPTION_V3', 'BNINCEPTION', 'SEResnet50'\n",
    "#AUX_LOGITS = True#False, only for 'INCEPTION_V3'\n",
    "print('====MODEL ACHITECTURE: %s===='%MODEL)\n",
    "\n",
    "device = set_n_get_device(\"0,1\", data_device_id=\"cuda:0\")#0, 1, 2, 3, IMPORTANT: data_device_id is set to free gpu for storing the model, e.g.\"cuda:1\"\n",
    "multi_gpu = [0,1] #None#[0, 1]#use 2 gpus\n",
    "\n",
    "SEED = 1234 #5678#4567#3456#2345#1234\n",
    "debug = False # if True, load 100 samples\n",
    "IMG_SIZE = (256,1600) #768#512#256\n",
    "BATCH_SIZE = 8\n",
    "NUM_WORKERS = 24\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count images in train/valid folder:  12568 1801\n",
      "Count of trainset (for training):  11311\n",
      "Count of validset (for training):  1257\n"
     ]
    }
   ],
   "source": [
    "train_dl, val_dl = prepare_trainset(BATCH_SIZE, NUM_WORKERS, SEED, IMG_SIZE, debug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1256, 4, 256, 1600)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y should be makeup\n",
    "y_valid = []\n",
    "for i, (image, masks) in enumerate(val_dl):\n",
    "    #if i==10:\n",
    "    #    break\n",
    "    truth = masks.to(device=device, dtype=torch.float)\n",
    "    y_valid.append(truth.cpu().numpy())\n",
    "y_valid = np.concatenate(y_valid, axis=0)\n",
    "y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = UNetResNet34(debug=False).cuda(device=device)\n",
    "\n",
    "checkpoint_path = '../checkpoint/UNetResNet34_256x1600_v1_seed1234/best.pth.tar'\n",
    "\n",
    "net, _ = load_checkpoint(checkpoint_path, net)\n",
    "if multi_gpu is not None:\n",
    "    net = nn.DataParallel(net, device_ids=multi_gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use TTA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:2457: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20min 13s, sys: 3min 35s, total: 23min 48s\n",
      "Wall time: 1min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_valid = predict_proba(net, val_dl, device, multi_gpu=multi_gpu, mode='valid', tta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1256, 4, 256, 1600), (1256, 4, 256, 1600))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.shape, preds_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('prediction/UNetResNet34_512_v1_seed5678__preds_valid.pkl', 'wb') as f:\n",
    "#     pickle.dump(preds_valid, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## ensemble logits\n",
    "# with open('prediction/UNetResNet34_512_v1_seed1234__preds_valid.pkl', 'rb') as f:\n",
    "#     preds_valid_1234 = pickle.load(f)\n",
    "# with open('prediction/UNetResNet34_512_v1_seed2345__preds_valid.pkl', 'rb') as f:\n",
    "#     preds_valid_2345 = pickle.load(f)\n",
    "# with open('prediction/UNetResNet34_512_v1_seed3456__preds_valid.pkl', 'rb') as f:\n",
    "#     preds_valid_3456 = pickle.load(f)\n",
    "# with open('prediction/UNetResNet34_512_v1_seed4567__preds_valid.pkl', 'rb') as f:\n",
    "#     preds_valid_4567 = pickle.load(f)\n",
    "# with open('prediction/UNetResNet34_512_v1_seed5678__preds_valid.pkl', 'rb') as f:\n",
    "#     preds_valid_5678 = pickle.load(f)\n",
    "\n",
    "# preds_valid = inverse_sigmoid((sigmoid(preds_valid_1234) + sigmoid(preds_valid_2345) + sigmoid(preds_valid_3456) + \\\n",
    "#                sigmoid(preds_valid_4567) + sigmoid(preds_valid_5678)) / 5)\n",
    "\n",
    "# del preds_valid_1234, preds_valid_2345, preds_valid_3456, preds_valid_4567, preds_valid_5678\n",
    "# gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## search for best thresholds\n",
    "def calculate_dice(logit, truth, EMPTY_THRESHOLD=[200,1500,1500,2000], MASK_THRESHOLD=0.22):\n",
    "    n, c = truth.shape[0], truth.shape[1]\n",
    "    logit = sigmoid(logit).reshape(n*c, -1)\n",
    "    truth = truth.reshape(n*c, -1)\n",
    "    pred = (logit>MASK_THRESHOLD).astype(np.int)\n",
    "    EMPTY_THRESHOLD_4ch = np.concatenate([EMPTY_THRESHOLD]*n)#.reshape(-1, 1)\n",
    "    pred_clf = (pred.sum(axis=1)<EMPTY_THRESHOLD_4ch).astype(np.int)\n",
    "    pred[pred_clf==1, ] = 0#.reshape(-1,)\n",
    "    return dice_overall(pred, truth)\n",
    "\n",
    "def dice_overall(pred_mask, truth_mask, eps=1e-8):\n",
    "    #n, c = truth_mask.shape[0], truth_mask.shape[1]\n",
    "    #pred_mask = pred_mask.reshape(n*c, -1)\n",
    "    #truth_mask = truth_mask.reshape(n*c, -1)\n",
    "    intersect = (pred_mask * truth_mask).sum(axis=1).astype(np.float)\n",
    "    union = (pred_mask + truth_mask).sum(axis=1).astype(np.float)\n",
    "    return ((2.0*intersect + eps) / (union+eps)).mean()\n",
    "\n",
    "# h, w = 256, 1600\n",
    "# print('For reference')\n",
    "# print('EMPTY_THRESHOLD: ', 400*(h/256)*(w/1600))\n",
    "# print('MASK_THRESHOLD: ', 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f48647c9a06c46a38eb9c3c0ce3b1e0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLF_EMPTY_THRESHOLD: [250, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920633\n",
      "CLF_EMPTY_THRESHOLD: [300, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920531\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n",
      "CLF_EMPTY_THRESHOLD: [400, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920849\n",
      "CLF_EMPTY_THRESHOLD: [450, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920703\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c904854f65d044baac622177e7de9993",
       "version_major": 2,
       "version_minor": 0
      },
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       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLF_EMPTY_THRESHOLD: [350, 800, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n",
      "CLF_EMPTY_THRESHOLD: [350, 900, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n",
      "CLF_EMPTY_THRESHOLD: [350, 1100, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d61cc604a381481c84996768cc66aa38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLF_EMPTY_THRESHOLD: [350, 1000, 800, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920825\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920933\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 1000, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920913\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 1100, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920302\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cd479877eef4a7eb7088b1feb259cf3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1300], MASK_THRESHOLD: 0.500000, dice_score: 0.920933\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1400], MASK_THRESHOLD: 0.500000, dice_score: 0.920933\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1500], MASK_THRESHOLD: 0.500000, dice_score: 0.920933\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1600], MASK_THRESHOLD: 0.500000, dice_score: 0.920933\n",
      "CLF_EMPTY_THRESHOLD: [350, 1000, 900, 1700], MASK_THRESHOLD: 0.500000, dice_score: 0.920904\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "EMPTY_THRESHOLD_candidate = [250, 1000, 1000, 1500]#np.arange(400, 500, 100) #for 256x1600\n",
    "MASK_THRESHOLD_candidate = [0.5]#np.arange(0.48, 0.50, 0.01)\n",
    "\n",
    "M, N = len(EMPTY_THRESHOLD_candidate), len(MASK_THRESHOLD_candidate)\n",
    "best_threshold_EMPTY = EMPTY_THRESHOLD_candidate\n",
    "best_threshold_MASK = None\n",
    "best_score = 0\n",
    "\n",
    "for MASK_THRESHOLD in MASK_THRESHOLD_candidate:\n",
    "\n",
    "    for EMPTY_THRESHOLD_ch1 in tqdm_notebook(np.arange(250, 500, 50)):\n",
    "        EMPTY_THRESHOLD = copy.deepcopy(best_threshold_EMPTY)\n",
    "        EMPTY_THRESHOLD[0] = EMPTY_THRESHOLD_ch1\n",
    "        \n",
    "        dice_score = calculate_dice(preds_valid, y_valid, EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "        print('CLF_EMPTY_THRESHOLD: %s, MASK_THRESHOLD: %f, dice_score: %f'%(str(EMPTY_THRESHOLD), MASK_THRESHOLD, dice_score))\n",
    "        if dice_score>best_score:\n",
    "            best_threshold_EMPTY = copy.deepcopy(EMPTY_THRESHOLD)\n",
    "            best_threshold_MASK = MASK_THRESHOLD\n",
    "            best_score = dice_score\n",
    "        \n",
    "    for EMPTY_THRESHOLD_ch2 in tqdm_notebook(np.arange(800, 1200, 100)):\n",
    "        EMPTY_THRESHOLD = copy.deepcopy(best_threshold_EMPTY)\n",
    "        EMPTY_THRESHOLD[1] = EMPTY_THRESHOLD_ch2\n",
    "\n",
    "        dice_score = calculate_dice(preds_valid, y_valid, EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "        print('CLF_EMPTY_THRESHOLD: %s, MASK_THRESHOLD: %f, dice_score: %f'%(str(EMPTY_THRESHOLD), MASK_THRESHOLD, dice_score))\n",
    "        if dice_score>best_score:\n",
    "            best_threshold_EMPTY = copy.deepcopy(EMPTY_THRESHOLD)\n",
    "            best_threshold_MASK = MASK_THRESHOLD\n",
    "            best_score = dice_score\n",
    "            \n",
    "    for EMPTY_THRESHOLD_ch3 in tqdm_notebook(np.arange(800, 1200, 100)):\n",
    "        EMPTY_THRESHOLD = copy.deepcopy(best_threshold_EMPTY)\n",
    "        EMPTY_THRESHOLD[2] = EMPTY_THRESHOLD_ch3\n",
    "\n",
    "        dice_score = calculate_dice(preds_valid, y_valid, EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "        print('CLF_EMPTY_THRESHOLD: %s, MASK_THRESHOLD: %f, dice_score: %f'%(str(EMPTY_THRESHOLD), MASK_THRESHOLD, dice_score))\n",
    "        if dice_score>best_score:\n",
    "            best_threshold_EMPTY = copy.deepcopy(EMPTY_THRESHOLD)\n",
    "            best_threshold_MASK = MASK_THRESHOLD\n",
    "            best_score = dice_score\n",
    "        \n",
    "    for EMPTY_THRESHOLD_ch4 in tqdm_notebook(np.arange(1300, 1800, 100)):\n",
    "        EMPTY_THRESHOLD = copy.deepcopy(best_threshold_EMPTY)\n",
    "        EMPTY_THRESHOLD[3] = EMPTY_THRESHOLD_ch4\n",
    "\n",
    "        dice_score = calculate_dice(preds_valid, y_valid, EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "        print('CLF_EMPTY_THRESHOLD: %s, MASK_THRESHOLD: %f, dice_score: %f'%(str(EMPTY_THRESHOLD), MASK_THRESHOLD, dice_score))\n",
    "        if dice_score>best_score:\n",
    "            best_threshold_EMPTY = copy.deepcopy(EMPTY_THRESHOLD)\n",
    "            best_threshold_MASK = MASK_THRESHOLD\n",
    "            best_score = dice_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([350, 1000, 900, 1500], 0.5, 0.9209332792760889)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_threshold_EMPTY, best_threshold_MASK, best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_mask(logit, EMPTY_THRESHOLD, MASK_THRESHOLD):\n",
    "    \"\"\"Transform each prediction into mask.\n",
    "    input shape: (256, 256)\n",
    "    \"\"\"\n",
    "    #pred mask 0-1 pixel-wise\n",
    "    #n = logit.shape[0]\n",
    "    #IMG_SIZE = logit.shape[-1] #256\n",
    "    #EMPTY_THRESHOLD = 100.0*(IMG_SIZE/128.0)**2 #count of predicted mask pixles<threshold, predict as empty mask image\n",
    "    #MASK_THRESHOLD = 0.22\n",
    "    #logit = torch.sigmoid(torch.from_numpy(logit)).view(n, -1)\n",
    "    #pred = (logit>MASK_THRESHOLD).long()\n",
    "    #pred[pred.sum(dim=1) < EMPTY_THRESHOLD, ] = 0 #bug here, found it, the bug is input shape is (256, 256) not (16,256,256)\n",
    "    logit = sigmoid(logit)#.reshape(n, -1)\n",
    "    pred = (logit>MASK_THRESHOLD).astype(np.int)\n",
    "    if pred.sum() < EMPTY_THRESHOLD:\n",
    "        return np.zeros(pred.shape).astype(np.int)\n",
    "    else:\n",
    "        return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## visualize predicted masks\n",
    "start = 1\n",
    "rows = 10\n",
    "\n",
    "cmaps = {0: \"Reds\", 1: \"Blues\", 2: \"Greens\", 3: \"Oranges\"}\n",
    "titles = {0: \"Class=1\", 1: \"Class=2\", 2: \"Class=3\", 3: \"Class=4\"}\n",
    "\n",
    "cnt = 0\n",
    "for idx, (img, mask) in enumerate(val_dl):\n",
    "    if idx<start:\n",
    "        continue\n",
    "    mask = mask.numpy()\n",
    "    img = img.numpy()\n",
    "    for j in range(BATCH_SIZE):#BATCH_SIZE=8\n",
    "        not_empty = mask[j].reshape(4, -1).sum()>0\n",
    "        if not_empty:\n",
    "            cnt+=1\n",
    "            ch = mask[j].reshape(4, -1).sum(axis=1).argmax()\n",
    "            pred_mask = predict_mask(preds_valid[idx*BATCH_SIZE+j][ch], EMPTY_THRESHOLD, MASK_THRESHOLD)#EMPTY_THRESHOLD=0\n",
    "            #if pred_mask.sum()==0:\n",
    "            #    continue\n",
    "            fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(15, 8))\n",
    "            #ax0.imshow(img[j][0], cmap='gray')\n",
    "            ax0.imshow(mask[j][ch], cmap=cmaps[ch], alpha=0.2)\n",
    "            #ax1.imshow(img[j][0], cmap='gray')\n",
    "            ax1.imshow(pred_mask, cmap=cmaps[ch], alpha=0.2)\n",
    "            if not_empty.item():\n",
    "                ax0.set_title('Targets(Class=%d)'%ch)\n",
    "            else:\n",
    "                ax0.set_title('Targets(Empty)')\n",
    "            ax1.set_title('Predictions')\n",
    "        if cnt>rows:\n",
    "            break\n",
    "    if cnt>rows:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is moved to kaggle kernel=============predict the testset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from dataset.dataset import prepare_testset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1801, 'acecbd93a.jpg')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fnames = [f.split('/')[-1] for f in glob.glob('../data/raw/test/*')]\n",
    "len(test_fnames), test_fnames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dl = prepare_testset(BATCH_SIZE, NUM_WORKERS, IMG_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use TTA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1386: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
      "/home/endi.niu/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:2457: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 40min 47s, sys: 12min 36s, total: 53min 23s\n",
      "Wall time: 3min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "preds_test = predict_proba(net, test_dl, device, multi_gpu=multi_gpu, mode='test', tta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# ##for ensemble\n",
    "# net = UNetResNet34(debug=False).cuda(device=device)\n",
    "# checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed1234/best.pth.tar'\n",
    "# net, _ = load_checkpoint(checkpoint_path, net)\n",
    "# preds_test_1234 = predict_proba(net, test_dl, device, multi_gpu=False, mode='test', tta=True)\n",
    "# print('seed1234 complete')\n",
    "\n",
    "# net = UNetResNet34(debug=False).cuda(device=device)\n",
    "# checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed2345/best.pth.tar'\n",
    "# net, _ = load_checkpoint(checkpoint_path, net)\n",
    "# preds_test_2345 = predict_proba(net, test_dl, device, multi_gpu=False, mode='test', tta=True)\n",
    "# print('seed2345 complete')\n",
    "\n",
    "# net = UNetResNet34(debug=False).cuda(device=device)\n",
    "# checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed3456/best.pth.tar'\n",
    "# net, _ = load_checkpoint(checkpoint_path, net)\n",
    "# preds_test_3456 = predict_proba(net, test_dl, device, multi_gpu=False, mode='test', tta=True)\n",
    "# print('seed3456 complete')\n",
    "\n",
    "# net = UNetResNet34(debug=False).cuda(device=device)\n",
    "# checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed4567/best.pth.tar'\n",
    "# net, _ = load_checkpoint(checkpoint_path, net)\n",
    "# preds_test_4567 = predict_proba(net, test_dl, device, multi_gpu=False, mode='test', tta=True)\n",
    "# print('seed4567 complete')\n",
    "\n",
    "# net = UNetResNet34(debug=False).cuda(device=device)\n",
    "# checkpoint_path = 'checkpoint/UNetResNet34_512_v1_seed5678/best.pth.tar'\n",
    "# net, _ = load_checkpoint(checkpoint_path, net)\n",
    "# preds_test_5678 = predict_proba(net, test_dl, device, multi_gpu=False, mode='test', tta=True)\n",
    "# print('seed5678 complete')\n",
    "\n",
    "# with open('prediction/UNetResNet34_512_v1__preds_test.pkl', 'wb') as f:\n",
    "#     pickle.dump([preds_test_1234, preds_test_2345, preds_test_3456, preds_test_4567, preds_test_5678], \n",
    "#                 f)\n",
    "\n",
    "# preds_test = inverse_sigmoid((sigmoid(preds_test_1234) + sigmoid(preds_test_2345) + sigmoid(preds_test_3456) + \\\n",
    "#                sigmoid(preds_test_4567) + sigmoid(preds_test_5678)) / 5)\n",
    "\n",
    "# del preds_test_1234, preds_test_2345, preds_test_3456, preds_test_4567, preds_test_5678\n",
    "# gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1801, 4, 256, 1600)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## visualize predicted masks\n",
    "start = 0\n",
    "total = 19\n",
    "\n",
    "fig=plt.figure(figsize=(15, 20))\n",
    "cnt = 0\n",
    "for idx, img in enumerate(test_dl):\n",
    "    if idx<start:\n",
    "        continue\n",
    "    for j in range(BATCH_SIZE):#BATCH_SIZE=8\n",
    "        cnt+=1\n",
    "        ch = preds_test[idx*BATCH_SIZE+j].reshape(4, -1).sum(axis=1).argmax()\n",
    "        pred_mask = predict_mask(preds_test[idx*BATCH_SIZE+j][ch], EMPTY_THRESHOLD, MASK_THRESHOLD)#EMPTY_THRESHOLD=0\n",
    "        #if pred_mask.float().mean()==0:\n",
    "        #    continue\n",
    "        ax = fig.add_subplot(5, 4, cnt)\n",
    "        plt.imshow(img[j][0].numpy(), cmap='gray')\n",
    "        plt.imshow(pred_mask, alpha=0.2, cmap=cmaps[ch])\n",
    "        if pred_mask.sum()>0:\n",
    "            plt.title('Predict (Class=%d)'%ch)\n",
    "        else:\n",
    "            plt.title('Predict Empty')\n",
    "        if cnt>total:\n",
    "            break\n",
    "    if cnt>total:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PIL\n",
    "from dataset.mask_functions import mask2rle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0ccc89dfb5845098879034a39bbf450",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1801), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "7204\n",
      "CPU times: user 7min 6s, sys: 12.2 s, total: 7min 19s\n",
      "Wall time: 27.5 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageId_ClassId</th>\n",
       "      <th>EncodedPixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acecbd93a.jpg_1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acecbd93a.jpg_2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acecbd93a.jpg_3</td>\n",
       "      <td>390758 38 390801 58 390861 2 390882 14 390918 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acecbd93a.jpg_4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>db9d34c52.jpg_1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ImageId_ClassId                                      EncodedPixels\n",
       "0  acecbd93a.jpg_1                                                   \n",
       "1  acecbd93a.jpg_2                                                   \n",
       "2  acecbd93a.jpg_3  390758 38 390801 58 390861 2 390882 14 390918 ...\n",
       "3  acecbd93a.jpg_4                                                   \n",
       "4  db9d34c52.jpg_1                                                   "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#### Step 1: Generate rle encodings (images are first converted to the original size)\n",
    "rles = []\n",
    "for p in tqdm_notebook(preds_test):#p is logit from model\n",
    "    for ch in range(4):\n",
    "        pred_mask = predict_mask(p[ch], EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "        rles.append(mask2rle(pred_mask))\n",
    "#         if pred_mask.sum()>0:#predicted non-empty mask\n",
    "#             im = PIL.Image.fromarray((pred_mask.T*255).astype(np.uint8)).resize((1024,1024))\n",
    "#             im = np.asarray(im)\n",
    "#             rles.append(mask2rle(im, 1024, 1024))\n",
    "#         else: rles.append('-1')\n",
    "\n",
    "img_id_ch = []\n",
    "for fname in test_fnames:\n",
    "    for i in range(4):\n",
    "        img_id_ch.append(fname+'_%d'%(i+1))\n",
    "\n",
    "sub_df = pd.DataFrame({'ImageId_ClassId': img_id_ch, 'EncodedPixels': rles})\n",
    "print(len(sub_df.index))\n",
    "sub_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(preds_test.shape)\n",
    "\n",
    "#pred_mask = predict_mask(preds_test[0][2], EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "#mask2rle(pred_mask)\n",
    "#pred_mask.shape\n",
    "\n",
    "#sub_df.loc[sub_df.ImageId_ClassId=='01b47d973.jpg_3', 'EncodedPixels'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sub_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19903"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the correctness of transformation\n",
    "pred_mask = predict_mask(preds_test[32][2], EMPTY_THRESHOLD, MASK_THRESHOLD)\n",
    "pred_mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb1952b0588>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pred_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sub_df.to_csv('../submission/0913_unet_seed1234_tta_v1_400_048.csv.gz', index=False, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11937812326485286"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sub_df.EncodedPixels!='').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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